The Potentials of Recommender Systems Challenges for Student Learning

F. Hopfgartner, A. Lommatzsch, B. Kille, M. Larson, T. Brodt, P. Cremonesi, A Karatzoglou

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Increasingly, educators make use of learning-by-doing approaches to teach studentsof STEM programmes the skills that they need to become successful incareers in research and development. However, we argue that the technicalchallenges addressed in these programmes are often too limited and thereforedo not support the students in gaining the more advanced skill sets required tothrive in our technology-oriented economy. We therefore suggest to incorporaterealistic and complex challenges that model real-world problems faced inindustrial settings. Focusing on the domain of recommender systems, we seepotentials in embedding recommender systems challenges to enhance studentlearning to teach students the skills required by modern data scientists.
Original languageEnglish
Title of host publicationProceedings of CiML'16
Subtitle of host publicationChallenges in Machine Learning: Gaming and Education 2016
Number of pages2
Publication statusPublished - 2016
EventCiML 2016 - Challenges in Machine Learning: Gaming and Education - Barcelona, Spain
Duration: 9 Dec 20169 Dec 2016


WorkshopCiML 2016 - Challenges in Machine Learning

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